Abstract Body (Do not enter title and authors here): Background:
Recent advancements have seen deep learning models help differentiate echocardiography images of patients with heart failure with preserved ejection fraction (HFpEF) from normal controls. Our aim was to develop a model capable of detecting early signs of heart failure in asymptomatic patients with a normal ejection fraction.

Methods:
We employed the TimeSformer, a video transformer model that classifies video data using a novel attention-based mechanism. This self-attention mechanism that diverges from traditional convolutional neural networks (CNNs). It focuses on relevant parts of the video across both space and time, split into spatial attention, which processes each frame individually, and temporal attention, which integrates information across different frames. The training and validation of the TimeSformer model were conducted on the same dataset of echocardiography videos from 50 normal controls and 50 patients diagnosed with HFpEF, employing 5-fold cross-validation to ensure robust performance evaluation.

Results:
The TimeSformer model effectively identified HFpEF in patients, as all diagnosed with HFpEF were flagged as abnormal. The echocardiography assessment, along with NT pro BNP levels, supported the diagnoses, with patients showing NT pro BNP levels of 1016±32 pg/ml. Conversely, 46 out of 50 normal controls were correctly identified, with their NT pro BNP levels averaging 52±12 pg/ml. These controls remained asymptomatic.

Conclusions:
The TimeSformer model demonstrated capability in identifying subtle deviations indicative of incipient heart failure in videos of normal controls, despite normal NT pro BNP levels. This suggests potential for early detection of heart failure in asymptomatic individuals. Further longitudinal studies are necessary to determine whether individuals identified as abnormal develop heart failure symptoms subsequently.